liyang
ae28d55f81
Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER, documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight manifest/downloader. Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the 20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed (third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
123 lines
3.1 KiB
Python
Executable File
123 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Quick sanity checks for a local FoundationPose install (no inference)."""
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from __future__ import annotations
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import argparse
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import os
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import sys
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from pathlib import Path
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--demo-data",
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action="store_true",
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help="Also check default paths used by run_demo.py (demo_data/mustard0).",
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)
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args = parser.parse_args()
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repo_root = Path(__file__).resolve().parent
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os.chdir(repo_root)
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if str(repo_root) not in sys.path:
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sys.path.insert(0, str(repo_root))
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errors: list[str] = []
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warnings: list[str] = []
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print(f"Repository: {repo_root}")
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print(f"Python: {sys.version.split()[0]}")
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# --- PyTorch / CUDA ---
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try:
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import torch
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print(f"PyTorch: {torch.__version__}")
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print(f"CUDA available: {torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f"CUDA device: {torch.cuda.get_device_name(0)}")
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except Exception as e:
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errors.append(f"torch: {e}")
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# --- Core imports (same chain as run_demo / estimater) ---
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modules = [
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"pytorch3d",
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"nvdiffrast.torch",
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"trimesh",
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"open3d",
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"cv2",
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"warp",
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]
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for mod in modules:
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try:
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__import__(mod)
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print(f"import {mod}: ok")
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except Exception as e:
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errors.append(f"import {mod}: {e}")
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# --- Native extension ---
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build_dir = repo_root / "mycpp" / "build"
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so_files = list(build_dir.glob("mycpp*.so"))
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if not so_files:
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warnings.append(f"No mycpp extension found under {build_dir} (run bash build_all_conda.sh).")
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else:
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print(f"mycpp extension: {so_files[0].name}")
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try:
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sys.path.insert(0, str(build_dir))
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import mycpp
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print(f"import mycpp: ok ({mycpp})")
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except Exception as e:
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errors.append(f"import mycpp: {e}")
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try:
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import estimater
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print("import estimater: ok")
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except Exception as e:
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errors.append(f"import estimater: {e}")
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# --- Weights (hard-coded run names in learning/training/predict_*.py) ---
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weight_sets = [
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("scorer", "2024-01-11-20-02-45"),
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("refiner", "2023-10-28-18-33-37"),
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]
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weights_root = repo_root / "weights"
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for label, stamp in weight_sets:
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d = weights_root / stamp
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ckpt = d / "model_best.pth"
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cfg = d / "config.yml"
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missing = [p.name for p in (ckpt, cfg) if not p.is_file()]
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if missing:
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warnings.append(
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f"Weights ({label}, {stamp}): missing {', '.join(missing)} — download per readme Data prepare."
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)
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else:
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print(f"Weights ({label}, {stamp}): ok")
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# --- Optional demo bundle ---
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if args.demo_data:
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demo_scene = repo_root / "demo_data" / "mustard0"
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demo_mesh = demo_scene / "mesh" / "textured_simple.obj"
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if not demo_mesh.is_file():
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warnings.append(f"run_demo demo scene: missing {demo_mesh}")
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else:
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print(f"run_demo demo_data: ok ({demo_mesh.relative_to(repo_root)})")
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print()
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for w in warnings:
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print(f"WARNING: {w}")
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for e in errors:
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print(f"ERROR: {e}")
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# Non-zero only when imports or core checks fail; missing weights/data still exit 0.
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if errors:
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return 1
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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